چكيده به لاتين
Turbofan engine control system is one of the most important and advanced technologies. Traditional switching engine control algorithms have been used for decades without any specific conceptual change where they have been able to fulfill the fundamental control requirements of turbofan engines. At the same time, new and advanced control techniques, many of which have industrial applications, are being developed to meet the goals of future engines. One of these new control techniques that has been of interest to researchers in recent years is model predictive control (MPC). MPC considers input/output constraints in the production of control input signal while ensures the engine limit protection, which introduces it as a potential alternative approach for turbofan engines control. However, no comprehensive study has been published in the literature about the comparison of the performance of MPC with the application to the turbofan engine control and min-max algorithm which is traditionally used in turbofan engines with all of the necessary limits for safe operation.
In this thesis, model predictive control is designed, implemented and tested as a modern controller for application in a turbofan engine control system and its performance is compared with the min-max control algorithm. For this purpose, steady-state thermodynamic modeling of turbofan engine is first performed for performance analysis and dynamic modeling is performed for controller design. The thermodynamic model is then linearized using different numerical linearization methods at multiple operating points. The multivariable MPC and the min-max control algorithm are then designed for these operating points, taking into account the two inputs of fuel flow rate and bleed and all the essential constraints for the safe operation of the turbofan engine. Since MPC is a model-based control algorithm and the accuracy of the linear model is very important, one of the problems of the designed MPC for turbofan engines so far is the mismatch between the linear model and the plant (nonlinear thermodynamic model). In previous researches, the limits of the system was conservatively determined to address this problem. In this thesis, the feedback correction method is applied to eliminate the effect of the plant-model mismatch. The gains of linear regulators of the min-max algorithms are also calculated by applying the genetic algorithm optimization method to obtain the optimal fuel flow rate and to appropriately compare it with the MPC algorithm, which produces the optimal control signal. In addition to design and simulation, these two controllers are compared in terms of computational burden and hardware implementation. For this purpose, the appropriate hardware are selected and both controllers are implemented. One of the best approaches to ensure the accuracy of the implementation and the performance of the controller is to perform hardware in the loop (HIL) simulation. For this purpose, for the first time, a test bed is provided and HIL simulation is performed using both MPC and min-max algorithms. Finally, the performance of these two controllers from different points of view, including computer simulation, hardware implementation and HIL simulation, is compared, analyzed and evaluated. The results of this analysis demonstrate that the MPC significantly improves the response time of the system in comparison with min-max algorithm and guarantees the engine limit protection. In addition, it is indicated that application of feedback correction technique is quite effective to alleviate the effect of the plant-model mismatch. However, the computational burden of the MPC, due to the optimization process at each time step, is higher than the min-max algorithm, which requires research on the algorithm and the implementation process.
